Phenotyping new rapeseed lines based on multiple traits: Application of GT and GYT biplot analyses

Abstract The selection based on multiple traits enhances the crop cultivars merit to farmers. In this regard, 19 breeding lines as well as two commercial cultivars were studied using a randomized complete block design (RCBD) with three replications in three locations during the 2020–2021 growing season. In this study, to identify the association among different traits and to select the best rapeseed lines based on multiple traits, genotype × trait (GT) and genotype × yield × trait (GYT) biplot analyses were used. The results showed that using GYT biplot is more efficient than GT biplot. Based on the GYT biplot and superiority index (SI), the breeding lines G16 and G18 were considered as superior genotypes in combination with the agronomical traits, that is, 1000‐seed weight, number of seeds per pod, number of pods per plant, number of lateral branches, plant height, and pod length with seed yield, which represents a genetic gain in rapeseed breeding program. Based on seed yield combination with phenological traits (early maturity), the breeding line G15 was selected as the best one. Moreover, the line G2 was defined as the superior one in combination of seed yield with pod length. The results indicated that there is a potential for simultaneous genetic improvement of the characteristics (i.e., plant height, number of seeds per pod, early maturity) in rapeseed. Generally, the graphical method of the GYT biplot represented an efficient and practical new way to recognize superior genotypes based on multiple traits in rapeseed breeding programs.


| INTRODUC TI ON
Rapeseed is the second most important oilseed crop following soybean worldwide that is used in nutritional and pharmaceutical industries (Alizadeh et al., 2021). The seeds of rapeseed encompass oil, vitamin, minerals, and protein as essential materials for nutritional industries (Beyzi et al., 2019). Therefore, the selection of high-yielding genotypes is especially important in this plant. The selection of highyield genotypes is complex and difficult due to low heritability and the existence of the interactions of seed yield (SY) genotype × environment (Gholizadeh & Dehghani, 2016, 2017. Agronomic traits and yield components have relatively high heritability. Therefore, plant breeders prefer selection indirectly using yield components and agronomic traits. The selection based on multiple traits enhances the crop cultivars merit to farmers (Mohammadi, 2019).
It is very difficult to develop superior genotypes considering all studied traits. For this purpose, the GYT biplot method recently has been introduced by Yan and Frégeau-Reid (2018) for the genotypes selection based on multiple traits. The GYT methodology provides a superiority index (SI) for evaluating genotypes based on all yieldtrait combinations and identifies the weaknesses and strengths of each genotype (Mohammadi, 2019). This method for the evaluation of the genotypes based on multiple traits was used in different crops by Yan and Frégeau-Reid (2018) in oats, Kendal (2019) in durum wheat, Boureima and Abdoua (2019) in sesame, Da et al. (2020) in cowpea, Hudzenko et al. (2021) in barley, Peixoto et al. (2022) in cotton, and Gouveia et al. (2020), Gouveia et al. (2022) in Urochloa sp.
So far, GYT biplot analysis had not been used in rapeseed breeding for examining the relationships between different traits and selecting genotypes based on different traits, and certainly this is the novelty for this paper. Therefore, the aims of this research were (1) to examine the association between different traits and (2) to select superior rapeseed lines based on the combination of agronomic traits with SY.

| MATERIAL S AND ME THODS
In this study, 19 new rapeseed lines along with Dalgan and RGS003 cultivars were evaluated at three locations with different climates using a randomized complete block design (RCBD) with three replications during growing season (2020-2021). The names and origins of the genotypes are given in Table 1. Table 2 Table 3.

| Statistical analysis
The data were first analyzed for normality test by Shapiro-Wilk test method using SPSS 19 software (SPSS, 2010

| GT biplot methodology
The data were graphically analyzed using the first two principal components derived from singular value decomposition (SVD). The GT biplot was carried out by GGEbiplot software (Yan & Rajcan, 2002).
Further information and detailed description on the GT biplot method are available in Yan and Kang′s (2003) review. The results achieved from this analysis were utilized (1) to investigate the interrelationships between different traits and (2) to select superior rapeseed lines based on the multiple traits.

| GYT biplot methodology
The GYT biplot methodology was performed according to the procedure of Yan and Frégeau-Reid (2018 While for traits of DFS, DEF, and DPM, in which the low value is desirable, the value for each trait was divided by the yield (e.g., SY/DPM).
The GYT biplot method was performed in multilocation trials for multitraits data of genotypes. The GYT biplot was carried out by where P ij is the standardized value of genotype i for yield-trait combination j in the standardized table, T ij is the original value of genotype i for yield-trait combination j, T j is the mean across genotypes for yield-trait combination j, and S j is the standard deviation for yield-trait combination j.
The results obtained from the GYT biplot methodology was used (1) to investigate the interrelationships between different traits and (2) to select superior rapeseed lines based on the yield-trait combinations.

| Principal component analysis for GT data
Data presented in Table S1 and graphically shown in Figure S1 revealed that the principal component analysis grouped the measured variables into three main components, which generally accounted for 68.5% of the total variation of data. The number of significant principal components was selected based on the eigenvalue higher than 1. According to this criterion, the first three principal components were selected because subsequent eigenvalues were all <1.

TA B L E 2 Agroclimatic characteristics of the locations studied in this research
The first, second, and third principal components are accounted for 31.6%, 24.0%, and 12.9%, respectively, of the variation in data. The

| The GT biplot for grouping the genotypes
One of the most useful applications of GT biplot is the polygon view which led to identify the genotypes containing one or more traits with the highest value. The vertex genotype in each sector of the polygon view is the best one in the test trait(s) that falls within that particular sector. According to Figure

| Principal component analysis for GYT data
Principal component analysis indicated that the first and second principal components were accounted for 91.0% of the total variation in the yield-trait combinations data (Table S2 and Figure S2).

| The GYT biplot for displaying the relationships among the yield-trait combinations
According to the vector view of the GYT biplot (Figure 4)

| The GYT biplot view to compare the studied genotypes with the ideal genotype
A ranking biplot that compare genotypes with an ideal genotype is represented in Figure 5. Accordingly, genotypes with the closest distance from the ideal genotype (concentric circles) were considered superior ones. On the other hand, genotypes with the furthest distance to the ideal genotype were nominated as the most undesired ones. Based on Figure 5, the breeding lines G16, G18, and G15 with lowest distance to the hypothetical ideal genotype were defined as the best genotypes, and line G8 due to its maximum distance to the hypothetical ideal genotype is considered as the most unfavorable genotype.

| Ranking genotypes based on their superiority
According to the average tester coordinate (ATC) view of the GYT biplot, lines G16 and G18 followed by G15 were defined as the superior ones in terms of all yield-trait combinations, while the weakest line was G8 ( Figure 6). Also, based on the SI, the genotypes were ranked considering the combinations of all yield-trait ( ranking of genotypes from the most desirable to the most undesirable genotypes is as follows: G18 ˃ G16 ˃ G15 ˃ G2 ˃ G20 ˃ G1 ˃ G 5 ˃ G11 ˃ G2 ˃ G1 ˃ G7 ˃ G12 ˃ G13 ˃ G19 ˃ G3 ˃ G14 ˃ G10 ˃ G1 7 ˃ G9 ˃ G9 ˃ G4 ˃ G8.

| DISCUSS ION
Oilseeds breeding and cultivation are necessary to increase yield in Iran because the major percent of vegetable oil is imported to F I G U R E 1 Polygon view of the genotype × trait biplot of rapeseed genotypes. DEF, days to end of flowering; DFS, days to flowering starting; DPM, days to physiological maturity; NLB, number of lateral branches; NPP, number of pods per plant; NSP, number of seeds per pod; PH, plant height; PL, pod length; SY, seed yield; TSW, 1000-seed weight. Refer to Table 1 for genotypes name.   (Gholizadeh & Dehghani, 2016). The GT biplot method has previously been used to compare genotypes based on multiple traits and to understand the interrelationship between yield and traits (morphological, physiological, and quality characters) in different crops (Dolatabad et al., 2010;Malik et al., 2014;Santana, Flores, et al., 2021;Santana, Ramos, et al., 2021;Santos et al., 2021;Yan & Rajcan, 2002). A potential constraint of the GT biplot method is that it may fail to explain most of the variation and therefore fail to display all patterns of the data and this is most likely to occur with large datasets, small main effects, and complex interactions. Even when this is the case, it can be ensured that the biplot of PC1 versus PC2 still displays the most important linear patterns of the data and the pattern is estimated by the total variation of the tester-centered data minus the noise, which is estimated by the total degrees of freedom, multiplied by the error mean square and can be estimated from replicated data (Yan & Kang, 2003).

F I G U R E 2
The GT biplot method is not able to distinguish the effect of all the traits on yield combination, while the GYT biplot method recently improved to eliminate this deficiency. The GYT biplot method has been introduced as an effective and comprehensive method that graphically identifies the strengths and weaknesses of each genotype and provides a SI for the evaluation of genotypes based on combining all the traits with yield (Kendal, 2019;Yan & Frégeau-Reid, 2018). This methodology was used by a few researchers for evaluating genotypes based on multiple traits combined with SY, and so far had not been used in rapeseed breeding programs, and certainly, this is the novelty for this paper.
The results showed that the use of the GYT biplot method has more privilege than the GT biplot method in rapeseed breeding studies. Based on the GYT biplot and SI, the breeding lines G16 and G18 were ranked as the superiors in the yield combination with agronomical traits, that is, PH, NLB, PL, NPP, NSP, and TSW with SY. The breeding line G15 was the best in combining SY with phenological traits (DFS, DEF, and DPM). Also, the line G2 was the superior one in SY combination with PL. In contrast, G8 was ranked the poorest. Selection of the superior genotypes based on multiple traits could enhance the value of crop cultivars to farmers. Hence, the breeding lines G16, G18, and G15 can be recommended for cultivation. Also, according to the GYT biplot, there was a positive correlation between all yield-trait combinations. This is an advantage of the GYT biplot method in comparison with the GT biplot. The other benefit of the GYT biplot is that it could be utilized in excessive features identification that decrease costs in traits measuring of field experiments without compromising accuracy. Therefore, a high positive relationship between NLB and NPP shows that one (i.e., NLB) of these traits can be used for the selection criterion. Similarly, the high relationship between PH, NSP, DFS, DEF, and DPM indicates that one (i.e., DPM) of these traits is a suitable selection criterion.

| CON CLUS ION
This study is unique, in that the GYT biplot method is used as a comprehensive and effective method for the evaluation and selection of rapeseed lines based on multiple traits. The results indicated that genotype selection could be possible considering multiple traits that led to enhance genetic material in rapeseed breeding programs. The breeding lines G16, G18, and G15 ranked as the superior ones in the combination of all agronomical traits with SY, showing a genetic gain in the rapeseed breeding program. Also our results revealed simultaneous potential genetic improvement of the traits (i.e., PH, NSP, early maturity) in rapeseed.

ACK N OWLED G M ENTS
We thank the Seed and Plant Improvement Institute (SPII), Karaj, Iran for their support by grant and genetic material provision.

FU N D I N G I N FO R M ATI O N
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

CO N FLI C T O F I NTE R E S T
The authors declare no conflict of interest.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are available from the corresponding author upon reasonable request.

E TH I C S S TATEM ENT
This study does not involve any human or animal testing.

I N FO R M E D CO N S E NT
Written informed consent was obtained from all study participants.